Emotion recognition from text using semantic labels and separable mixture models

Chung Hsien Wu, Ze Jing Chuang, Yu Chung Lin

Research output: Contribution to journalArticlepeer-review

158 Citations (Scopus)


This study presents a novel approach to automatic emotion recognition from text. First, emotion generation rules (EGRs) are manually deduced from psychology to represent the conditions for generating emotion. Based on the EGRs, the emotional state of each sentence can be represented as a sequence of semantic labels (SLs) and attributes (ATTs); SLs are defined as the domain-independent features, while ATTs are domain-dependent. The emotion association rules (EARs) represented by SLs and ATTs for each emotion are automatically derived from the sentences in an emotional text corpus using the a priori algorithm. Finally, a separable mixture model (SMM) is adopted to estimate the similarity between an input sentence and the EARs of each emotional state. Since some features defined in this approach are domain-dependent, a dialog system focusing on the students' daily expressions is constructed, and only three emotional states, happy, unhappy, and neutral, are considered for performance evaluation. According to the results of the experiments, given the domain corpus, the proposed approach is promising, and easily ported into other domains.

Original languageEnglish
Pages (from-to)165-182
Number of pages18
JournalACM Transactions on Asian Language Information Processing
Issue number2
Publication statusPublished - 2006 Jun

All Science Journal Classification (ASJC) codes

  • Computer Science(all)


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